Prediction of near-term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

Wheat is a major crop worldwide, mainly cultivated for human consumption and animal feed. Grain quality is paramount in determining its value and downstream use. While we know that climate change threatens global crop yields, a better understanding of impacts on wheat end-use quality is also critical. Combining quantitative genetics with climate model outputs, we investigated UK-wide trends in genotypic adaptation for wheat quality traits. In our approach, we augmented genomic prediction models with environmental characterisation of field trials to predict trait values and climate effects in historical field trial data between 2001 and 2020. Addition of environmental covariates, such as temperature and rainfall, successfully enabled prediction of genotype by environment interactions (G × E), and increased prediction accuracy of most traits for new genotypes in new year cross validation. We then extended predictions from these models to much larger numbers of simulated environments using climate scenarios projected under Representative Concentration Pathways 8.5 for 2050–2069. We found geographically varying climate change impacts on wheat quality due to contrasting associations between specific weather covariables and quality traits across the UK. Notably, negative impacts on quality traits were predicted in the East of the UK due to increased summer temperatures while the climate in the North and South-west may become more favourable with increased summer temperatures. Furthermore, by projecting 167,040 simulated future genotype–environment combinations, we found only limited potential for breeding to exploit predictable G × E to mitigate year-to-year environmental variability for most traits except Hagberg falling number. This suggests low adaptability of current UK wheat germplasm across future UK climates. More generally, approaches demonstrated here will be critical to enable adaptation of global crops to near-term climate change.     

Structures of randomly generated mutants of T4 lysozyme show that protein stability can be enhanced by relaxation of strain and by improved hydrogen bonding via bound solvent.

The structures of three mutants of bacteriophage T4 lysozyme selected using a screen designed to identify thermostable variants are described. Each of the mutants has a substitution involving threonine. Two of the variants, Thr 26-->Ser (T26S) and Thr 151-->Ser (T151S), have increased reversible melting temperatures with respect to the wild-type protein. The third, Ala 93-->Thr (A93T), has essentially the same stability as wild type. Thr 26 is in the wall of the active-site cleft. Its replacement with serine results in the rearrangement of nearby residues, most notably Tyr 18, suggesting that the increase in stability may result from the removal of strain. Thr 151 in the wild-type structure is far from the active site and appears to sterically prevent the access of solvent to a preformed binding site. In the mutant, the removal of the methyl group allows access to the solvent binding site and, in addition, the Ser 151 hydroxyl rotates to a new position so that it also contributes to solvent binding. Residue 93 is in a highly exposed site on the surface of the molecule, and presumably is equally solvent exposed in the unfolded protein. It is, therefore, not surprising that the substitution Ala 93-->Thr does not change stability. The mutant structures show how chemically similar mutations can have different effects on both the structure and stability of the protein, depending on the structural context. The results also illustrate the power of random mutagenesis in obtaining variants with a desired phenotype.(ABSTRACT TRUNCATED AT 250 WORDS)     

Modeling of protein loops by simulated annealing.

A method is presented to model loops of protein to be used in homology modeling of proteins. This method employs the ESAP program of Higo et al. (Higo, J., Collura, V., & Garnier, J., 1992, Biopolymers 32, 33-43) and is based on a fast Monte Carlo simulation and a simulated annealing algorithm. The method is tested on different loops or peptide segments from immunoglobulin, bovine pancreatic trypsin inhibitor, and bovine trypsin. The predicted structure is obtained from the ensemble average of the coordinates of the Monte Carlo simulation at 300 K, which exhibits the lowest internal energy. The starting conformation of the loop prior to modeling is chosen to be completely extended, and a closing harmonic potential is applied to N, CA, C, and O atoms of the terminal residues. A rigid geometry potential of Robson and Platt (1986, J. Mol. Biol. 188, 259-281) with a united atom representation is used. This we demonstrate to yield a loop structure with good hydrogen bonding and torsion angles in the allowed regions of the Ramachandran map. The average accuracy of the modeling evaluated on the eight modeled loops is 1 A root mean square deviation (rmsd) for the backbone atoms and 2.3 A rmsd for all heavy atoms.     

Protein structure dynamics by crosslinking mass spectrometry

Crosslinking mass spectrometry captures protein structures in solution. The crosslinks reveal spatial proximities as distance restraints, but do not easily reveal which of these restraints derive from the same protein conformation. This superposition can be reduced by photo-crosslinking, and adding information from protein structure models, or quantitative crosslinking reveals conformation-specific crosslinks. As a consequence, crosslinking MS has proven useful already in the context of multiple dynamic protein systems. We foresee a breakthrough in the resolution and scale of studying protein dynamics when crosslinks are used to guide deep-learning-based protein modelling. Advances in crosslinking MS, such as photoactivatable crosslinking and in-situ crosslinking, will then reveal protein conformation dynamics in the cellular context, at a pseudo-atomic resolution, and plausibly in a time-resolved manner.     

Using real-world data to predict health outcomes—The prediction design: Application and sample size planning

The gold standard for investigating the efficacy of a new therapy is a (pragmatic) randomized controlled trial (RCT). This approach is costly, time-consuming, and not always practicable. At the same time, huge quantities of available patient-level control condition data in analyzable format of (former) RCTs or real-world data (RWD) are neglected. Therefore, alternative study designs are desirable. The design presented here consists of setting up a prediction model for determining treatment effects under the control condition for future patients. When a new treatment is intended to be tested against a control treatment, a single-arm trial for the new therapy is conducted. The treatment effect is then evaluated by comparing the outcomes of the single-arm trial against the predicted outcomes under the control condition. While there are obvious advantages of this design compared to classical RCTs (increased efficiency, lower cost, alleviating participants’ fear of being on control treatment), there are several sources of bias. Our aim is to investigate whether and how such a design—the prediction design—may be used to provide information on treatment effects by leveraging external data sources. For this purpose, we investigated under what assumptions linear prediction models could be used to predict the counterfactual of patients precisely enough to construct a test and an appropriate sample size formula for evaluating the average treatment effect in the population of a new study. A user-friendly R Shiny application (available at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/ ) facilitates the application of the proposed methods, while a real-world application example illustrates them.     

蛋白质侧链的预测

根据Ｄｅｓｍｅｔ的死端排除定则制作了一种蛋白质侧链观测的快速软件系统,用一批实例结构数据和不同的侧链转子来检验上定则的可行性;并讨论预测的ＲＭＳ与溶剂可及性和结构元素的关系。     

A transformer architecture for retention time prediction in liquid chromatography mass spectrometry-based proteomics

Accurate retention time (RT) prediction is important for spectral library-based analysis in data-independent acquisition mass spectrometry-based proteomics. The deep learning approach has demonstrated superior performance over traditional machine learning methods for this purpose. The transformer architecture is a recent development in deep learning that delivers state-of-the-art performance in many fields such as natural language processing, computer vision, and biology. We assess the performance of the transformer architecture for RT prediction using datasets from five deep learning models Prosit, DeepDIA, AutoRT, DeepPhospho, and AlphaPeptDeep. The experimental results on holdout datasets and independent datasets exhibit state-of-the-art performance of the transformer architecture. The software and evaluation datasets are publicly available for future development in the field.     

Factors controlling the growth of field populations of Hydrodictyon reticulatum in New Zealand

Since Hydrodictyon reticulatum was introduced to New Zealand it has spread rapidly and produced persistent annual nuisance growths in areas where nuisance algal had not occurred previously. Field bioassays were conducted at 10 sites between August 1993 and February 1995 to evaluate the seasonal growth patterns and the factors controlling growth under natural conditions. H. reticulatum exhibited a strong seasonal growth pattern with growth rates up to 0.33 doublings d^-1 from August to March, are duction in growth rate in April and little or no growth from May to July. The H. reticulatum present in New Zealand has are latively low requirement for dissolved inorganic nitrogen (DIN) in comparison with other nuisance species, with its growth rate being saturated at 200 mg m^-3. This and the high affinity for DIN as shown by a K_s of 29 mg m^-3 have been key factors in the establishment of nuisance growths of H. reticulatum in New Zealand. This revised version was published online in August 2006 with corrections to the Cover Date.     

X-ray structure of recombinant horse L-chain apoferritin at 2.0 Å resolution: implications for stability and function

The X-ray structure of recombinant horse L-chain (rL) apoferritin, solved at 2.0?Å resolution with a final R factor of 17.9%, gives evidence that the residue at position 93 in the sequence is a proline and not a leucine, as found in earlier sequencing studies. The structure is isomorphous with other apoferritin structures, and we thus draw particular attention to those structural features which can be related to the stability and function of the protein. Analysis of hydrogen bonding and salt bridge interactions shows that dimers and tetramers are the most stable molecular entities within the protein shell: a result confirming earlier biophysical experiments. The stability of horse rL apoferritin to both dissociation into subunits at acidic pH values and to complete unfolding in guanidine chloride solutions is compared with that of other apoferritins. This emphasizes the role played by the salt bridge in the stability of this protein family. The horse rL apoferritin is significantly more resistant to denaturation than horse spleen ferritin, which in turn is more resistant than any human rH apoferritins, even those for which a salt bridge is restored. Finally, this structure determination not only establishes that a preformed pocket exists in L-chain apoferritin, at a site known to be able to bind porphyrin, but also underlines the particular function of a cluster of glutamic acids (E53, E56, E57 and E60) located at the entrance of this porphyrin-binding pocket.